Magnetic Field Gated and Current Controlled Spintronic Mem-transistor Neuron -based Spiking Neural Networks
arxiv(2024)
摘要
Spintronic devices, such as the domain walls and skyrmions, have shown
significant potential for applications in energy-efficient data storage and
beyond CMOS computing architectures. In recent years, spiking neural networks
have shown more bio-plausibility. Based on the magnetic multilayer spintronic
devices, we demonstrate the magnetic field-gated Leaky integrate and fire
neuron characteristics for the spiking neural network applications. The LIF
characteristics are controlled by the current pulses, which drive the domain
wall, and an external magnetic field is used as the bias to tune the firing
properties of the neuron. Thus, the device works like a gate-controlled LIF
neuron, acting like a spintronic Mem-Transistor device. We develop a LIF neuron
model based on the measured characteristics to show the device integration in
the system-level SNNs. We extend the study and propose a scaled version of the
demonstrated device with a multilayer spintronic domain wall magnetic tunnel
junction as a LIF neuron. using the combination of SOT and the variation of the
demagnetization energy across the thin film, the modified leaky integrate and
fire LIF neuron characteristics are realized in the proposed devices. The
neuron device characteristics are modeled as the modified LIF neuron model.
Finally, we integrate the measured and simulated neuron models in the 3-layer
spiking neural network and convolutional spiking neural network CSNN framework
to test these spiking neuron models for classification of the MNIST and FMNIST
datasets. In both architectures, the network achieves classification accuracy
above 96
properties, and promise for scalability. The presented devices show an
excellent properties for neuromorphic computing applications.
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